Please submit your resume in English.

At KnowBe4, we recognize that cybersecurity has always been a human problem. The Knowbe4 unified platform uses intelligent, adaptive systems to help manage risk for both people and AI agents, responding to threats, turning human behavior from the weakest link into the strongest defense.

As a Principal Software Engineering Manager, you'll lead a full-stack AI engineering team of data scientists, ML engineers, software engineers, and data engineers - building the models, services, and pipelines that power our products end-to-end. You'll set the technical direction across the stack, turn applied research into production systems, and grow the people doing the work.

Responsibilities:

• Leadership: Hire, mentor, and grow a high-performing full-stack AI engineering team spanning data science, ML engineering, software engineering, and data engineering.
• Technical Direction: Define the vision across the stack - model architectures, services, data pipelines, and the evaluation and production-readiness standards that hold it all together.
• AI Delivery: Drive the design and deployment of LLM and SLM based architectures and agentic systems - RAG, fine-tuning, retrieval, orchestration, and evaluation pipelines that hold up in production.
• Full-Stack Ownership: Own outcomes end-to-end - from data ingestion and feature pipelines, through modeling and serving, to the APIs and product surfaces that put AI in front of users.
• Partnership: Align teams around shared architectural principles and long-term technical goals. Partner closely with Product Management to align engineering designs with product requirements.
• Standards: Establish best practices for experimentation, model evaluation, observability, and responsible AI.
• Execution: Set clear goals, manage roadmaps, and deliver outcomes that move product metrics - not just model metrics.
• Domain Expertise: Stay deeply engaged with the cybersecurity landscape - emerging threats, evolving compliance requirements, and industry standards - so the team's work directly addresses the real-world challenges our customers face.

Requirements:

You lead from the front - still close enough to the modeling work to set technical direction, but focused on multiplying your team's impact.

• • 10+ years in data science, applied ML, and AI engineering, with 3+ years leading cross-functional teams that ship AI-powered products.
  • Proven track record building and deploying AI systems - foundation models, ensemble architectures, RAG, fine-tuning, Generative AI and LLM orchestration.
  • Strong hands-on background in ML / DL modeling, statistical analysis, and rigorous evaluation.
  • Comfort leading across the stack - data engineering, ML, and the backend services and APIs that productize it.
  • Expertise in Python and the modern data/ML stack; comfort designing on AWS.
  • Experience partnering with engineering on model serving, MLOps, and vector search.
  • Track record of mentoring, hiring, and developing senior IC talent across multiple disciplines.

• Bonus points if you have:

  • Background in cybersecurity, fraud, or other adversarial domains.
  • MS / PhD in CS, ML, or a related field, or publications in GenAI / NLP.

The Tech You'll Work With

• AI & LLMs: Anthropic Claude · AWS Bedrock · OpenAI · HuggingFace · PyTorch · LangChain · LangGraph
• Data & ML: Python · FastAPI · Pydantic · PySpark · Polars · Parquet · SageMaker · Vector DBs (PGvector, OpenSearch)
• Cloud & Infra: AWS Lambda · Batch · Glue · EventBridge · Terraform · GitLab CI/CD